Pragtee Tathe, Mohua Biswas, Anup Vibhute, Geeta Unhale, Mrunmayi Raut, and Papiya Biswas Datta
Abstract Human body is the magical creation of god. It carries many intercon- nected systems. Changes in one system replicate changes in another system. Due to such interconnection of systems, we can analyze one system by observing changes in another system. Iridology supports the same theory. Iridology tells the relation between iris and other systems present in the body. Evaluation can be done in the form of the iris that speaks about the physical condition of different body parts. In the proposed method by observing different iris images without doing any compli- cated and time-consuming test, we can perform diagnosis for the brain tumour. This method can be used as a pre diagnosis tool.
Keywords Iris
·
Iridology·
Pre diagnosis·
Brain tumour1 Introduction
One of the best creations of god is the human body. The human body contains many organs, and these organs are linked with each other. One of the important organs in the human body is the eye which contains many parts like sclera which is whitish outer background, iris which is middle central ring may be brown, black or blue and the black centre pupil. The branch of science which focuses on the study of the iris is called Iridology. Iridology, also known as iridodiagnosis, is a technique in which the iris images are analyzed with different aspects. The technique of iridology is based
P. Tathe (
B
)·M. Biswas·A. Vibhute·G. UnhaleSVERI’s College of Engineering, Pandharpur, India e-mail:[email protected]
Solapur University, Solapur, India G. Unhale
e-mail:[email protected] M. Raut
FTC COER Sangola, Solapur University, Solapur, India P. B. Datta
Devi Mahalaxmi Polytechnic College Titwala, Kalyan, Thane, India
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. M. Pawar et al. (eds.),Techno-Societal 2020,
https://doi.org/10.1007/978-3-030-69921-5_15
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Fig. 1 Iris chart for right and left iris
on the theory that different body parts are related to different regions in the iris. The human iris is categorized into 90 different regions, and each region replicates the status of different human body parts. Whenever there is a change in the body organs, it is reflected in the iris. The form of the iris varies according to the condition of body parts. The right eye iris replicated the status of right side body organs and left eye iris replicates the status of left body organs [1,2]. With several nerves connected, the iris is linked with body parts as shown in the iris chart in Fig.1. It indicates the distribution of different organs over the iris circle.
The well solidly organized fibers of iris are the indication of a healthy body. The unhealthy body has disturbed solidly organized fibers and makes them slackly as shown in Fig.2.
A brain tumour is a major disease that has affected over many million people across the globe; the rate of people getting affected will exponentially increase in the coming years. There are different methods for detecting the brain tumour like MRI scan, CT scan etc.; these techniques are time-consuming; it also needs specially trained persons to handle it. The major thing related to these techniques is that the patients at the initial stage are not going to look forward towards these techniques. The proposed project aims to construct a graphical user interface that allows any physician to predict brain health with user-friendly and fast diagnosis techniques. These techniques integrate various image processing steps together in order to complete the process of diagnosis as shown in Fig.3.
Implementation of Iridology for Pre Diagnosis of Brain Tumor 143
Fig. 2 Schematic representation of normal and abnormal human eye
2 Methodology
A. Capturing of eye images
In this approach, the images of the eye are collected by using a high-resolution camera and with these images a database is formed as shown in Fig.4. The database is segregated into two parts: one part contains images of iris of a healthy person, and
Fig. 3 Methodology for iris
diagnosis 1. Capturing of Eye Image
2. Pre-processing of Eye Image
3. Sectionalisation
4. Standardisation of iris Image
5. Taking ROI
6. Feature Extraction
7. Classification
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Fig. 4 Captured eye image
another part contains images of iris of brain tumour suffered person. The database can also be formed by using images collected from an online database such as CASIA database, MMU database, and UBIRIS database [1]. Normally, in-hospital ophthal- mology department is dealing with the iris images. For the proposed work database is created by taking the total 60 images out of those 30 images are of a normal person, and 30 images are of persons with a brain tumour. The below figure shows the captured image of an eye.
B. Pre-processing of eye images
The pre-processing is the next step in the methodology of the proposed system. In this stage, whatever the captured image is there, it has to pass through various filtering algorithms. After passing through these filtering algorithms, the image quality gets improved because this stage scales back the presence of noise within the iris image.
This stage is very important to make end results more relevant than first. For the iris images, adaptive filtering is used [1,3–5].
C. Sectionalisation
Sectionalisation is the process to seek out interior and exterior precincts of the iris.
The circular Hough transforms often won’t to comprehend the radius and centre coordinates of the pupil and iris sections. By subtracting pupil from the sclera, we will be able to fix the iris. Once the iris region is segmented from an eye, the subsequent step is to modify the iris region into fixed dimensions. After subtraction, we will get the iris pattern into a circular shape ring [1,6,7].
D. Standardization of the iris image
Daugman’s rubber sheet model (as represented in Fig.5) is employed for standard- ization of Iris ring after standardization of iris ring; the ring is transformed into a rectangular shape were to represent the points on the ring we can use x and y coordi- nates. The relation between preceding and the following image is specified with the relation between the coordinate systems [1,3,8].
Fig. 5 Daugman’s rubber sheet model
Implementation of Iridology for Pre Diagnosis of Brain Tumor 145
Fig. 6 Taking out ROI from a standardized iris image
E. Taking out ROI
With reference to the point mentioned in the introduction part that different sections of the iris are related with specific body parts so for performing analysis of brain tumour we need to crop the area from the normalized iris. As we have divided the iris area into sectors, we only need to crop the sector, which represents the brain area. In the circular iris, if we consider a specific point as a starting point then with respect to that, we have to select coordinates for a brain tumour. Depending upon which point we have selected as a starting point to convert into a rectangular shape, the coordinates values are going to change. If we compare the iris with the image of a clock, then the zone from 11 to 1 represents the brain area. So accordingly, we can take the region of interest [2,9] as shown in Fig.6.
F. Feature extraction
Once we get the significant region subsequently, we can find out different features for that region. The extracted features are as follows [1,3].
1. Mean: The average level of intensity of the image.
Mean:μ=
G−1
i=0
i p(i)
2. Variance: Variance describes the variation of intensity around the mean
V ar i ance:σ2=
G−1
i=0
(i−μ)2p(i)
3. Skewness: It is the measure of the asymmetry of the probability distribution of the random variable.
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Skewness:μ3=σ−3
G−1
i=0
(i−μ)3p(i) 4. Entropy: It is the amount of information present.
Entr opy:H = −
G−1
i=0
p(i)ln[p(i)]
5. Kurtosis: It is the measure of the flatness of the histogram of the image.
K ur t osi s:μ4=σ−4
G−1
i=0
[(i−μ)4p(i)]−3
6. Energy: It is the measure of the brightness of the histogram of the image
Ener gy:E =
G−1
i=0
[p(i)]2
G. Classification
Once we have calculated different features for normal as well as abnormal iris ROI images, the next step is to train the classifier. Nowadays there are different classi- fiers which we can use, but out of all support vector machines is more preferable because it has the kernel trick to transform our data and with the help of which an optimal boundary is detected for preferable outcomes. So nonlinear SVM classified the images into two classes; one indicates healthy human iris, and other indicates iris affected by a brain tumour [1,10].